falcon.py

import gym
import rocket_lander_gym
from stable_baselines import PPO2

env = gym.make('RocketLander-v0')
# Monitor the run
env = gym.wrappers.Monitor(env, "./video", force=True)
# Load the saved model
model = PPO2.load("ppo2-falcon.zip", env=None)
# Run the trained agent
obs = env.reset()
while True:
    env.render()
    action, _states = model.predict(obs)
    obs, rewards, done, info = env.step(action)
    if done:
      break